CN112799412A - Control method and control device of unmanned equipment - Google Patents
Control method and control device of unmanned equipment Download PDFInfo
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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Abstract
The present specification discloses a control method and a control device for an unmanned device, where the unmanned device may determine each target obstacle around and a traffic light corresponding to a location of the target obstacle, and determine a current driving state of the target obstacle according to the acquired obstacle information of the target obstacle for each target obstacle, and then determine an actual traffic light state corresponding to the traffic light according to the determined current driving state of each target obstacle, and control the unmanned device according to the actual traffic light state.
Description
Technical Field
The present disclosure relates to the field of unmanned driving, and in particular, to a control method and a control device for an unmanned device.
Background
In the field of unmanned driving, in order to ensure that the unmanned equipment can travel in the road in conformity with traffic regulations, it is necessary to determine the traffic light state of the traffic light, i.e., whether the traffic light is a green light or a red light, during the travel of the unmanned equipment.
In the prior art, the unmanned device may collect an image of the front and recognize the color of the traffic light in the image to determine the current traffic light state, but in practical applications, the traffic light may be blocked by a larger vehicle, or the image may not show the color of the current traffic light due to illumination, so that the traffic light state cannot be determined by the prior art.
Therefore, how to determine the traffic light state under the condition that the traffic light cannot be shot is an urgent problem to be solved.
Disclosure of Invention
The present specification provides a control method and a control device for an unmanned aerial vehicle, which partially solve the above problems in the prior art.
The technical scheme adopted by the specification is as follows:
the present specification provides a control method of an unmanned aerial vehicle device, including:
acquiring a preset electronic map, determining candidate obstacles around the unmanned equipment, screening interference obstacles from the candidate obstacles according to preset screening conditions, acquired obstacle information of each candidate obstacle and the electronic map, taking other obstacles except the interference obstacles in the candidate obstacles as target obstacles around the unmanned equipment, and determining traffic lights corresponding to the position where the unmanned equipment is located;
aiming at each target obstacle, determining the current corresponding running state of the target obstacle according to the acquired obstacle information of the target obstacle;
determining the actual traffic light state corresponding to the traffic light according to the determined current corresponding driving state of each target obstacle;
and controlling the unmanned equipment according to the actual traffic light state.
Optionally, determining candidate obstacles around the unmanned device specifically includes:
and if the unmanned equipment is monitored to be located in a set area containing traffic lights, determining each candidate obstacle around the unmanned equipment and located in the set area.
Optionally, the method for screening the interference obstacle from the candidate obstacles according to a preset screening condition, the acquired obstacle information of each candidate obstacle, and the electronic map specifically includes:
for each candidate obstacle, if the candidate obstacle is determined to be positioned at the intersection according to the acquired obstacle information of the candidate obstacle and the electronic map, and steering is performed according to the specified direction, the candidate obstacle is determined to be an interference obstacle; or
For each candidate obstacle, if the candidate obstacle stays in a roadside parking area according to the electronic map, determining the candidate obstacle as an interference obstacle; or
For each candidate obstacle, if the candidate obstacle is determined to be a non-motor vehicle according to the acquired obstacle information of the candidate obstacle, determining the candidate obstacle to be an interference obstacle; or
And for each candidate obstacle, if the candidate obstacle and the unmanned equipment are positioned in different lanes according to the electronic map and the acquired obstacle information of the candidate obstacle, determining that the candidate obstacle is an interference obstacle.
Optionally, determining an actual traffic light state corresponding to the traffic light according to the determined current corresponding driving state of each target obstacle, specifically including:
aiming at each target obstacle, determining a candidate traffic light state corresponding to the traffic light according to the current corresponding running state of the target obstacle;
and determining the actual traffic light state corresponding to the traffic light according to the candidate traffic light state corresponding to the traffic light determined aiming at each target obstacle.
Optionally, determining a candidate traffic light state corresponding to the traffic light according to the current driving state of the target obstacle specifically includes:
determining a current driving direction of the target obstacle as a first driving direction, and determining a current driving direction of the unmanned device as a second driving direction;
if the similarity between the first driving direction and the second driving direction is determined to fall into a first set similarity interval, when the target obstacle is in a moving state, determining that the candidate traffic light state corresponding to the traffic light is a green light, and when the target obstacle is in a static state, determining that the candidate traffic light state corresponding to the traffic light is a red light;
if the similarity between the first driving direction and the second driving direction is determined to fall into a second set similarity interval, when the target obstacle is in a moving state, the candidate traffic light state corresponding to the traffic light is determined to be a red light, and when the target obstacle is in a static state, the candidate traffic light state corresponding to the traffic light is determined to be a green light.
Optionally, when the target obstacle is currently in a moving state, before determining the candidate traffic light state corresponding to the traffic light, the method further includes:
and determining that the target barrier is positioned in the traffic intersection corresponding to the traffic light.
Optionally, when the target obstacle is currently in a stationary state, before determining the candidate traffic light state corresponding to the traffic light, the method further includes:
and determining that no other obstacles for blocking the target obstacle to run exist within the set distance in front of the target obstacle.
The present specification provides a control apparatus of an unmanned aerial vehicle, including:
the obstacle determining module is used for acquiring a preset electronic map, determining candidate obstacles around the unmanned equipment, screening interference obstacles from the candidate obstacles according to preset screening conditions, acquired obstacle information of each candidate obstacle and the electronic map, taking other obstacles except the interference obstacles in the candidate obstacles as target obstacles around the unmanned equipment, and determining traffic lights corresponding to the position where the unmanned equipment is located;
the driving state determining module is used for determining the current corresponding driving state of each target obstacle according to the acquired obstacle information of the target obstacle;
the traffic light state determining module is used for determining the actual traffic light state corresponding to the traffic light according to the determined driving state of each target barrier at the current time;
and the control module is used for controlling the unmanned equipment according to the actual traffic light state.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described control method of an unmanned aerial device.
The present specification provides an unmanned aerial vehicle comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-described method of controlling an unmanned aerial vehicle when executing the program.
The technical scheme adopted by the specification can achieve the following beneficial effects:
in the method for controlling the unmanned aerial vehicle provided by the present specification, the unmanned aerial vehicle may obtain a preset electronic map, determine candidate obstacles around the unmanned aerial vehicle, screen an interfering obstacle from the candidate obstacles according to a preset screening condition, acquired obstacle information of each candidate obstacle and the electronic map, use other obstacles except the interfering obstacle in the candidate obstacles as target obstacles around the unmanned aerial vehicle, and determine a traffic light corresponding to the located position. Then, for each target obstacle, determining a current corresponding driving state of the target obstacle according to the acquired obstacle information of the target obstacle, determining an actual traffic light state corresponding to the traffic light according to the determined current corresponding driving state of each target obstacle, and further controlling the target obstacle according to the actual traffic light state.
According to the method, the traffic light state of the traffic light corresponding to the position of the unmanned equipment can be determined by the unmanned equipment according to the driving state of each target obstacle around, namely, whether the traffic light facing the unmanned equipment is a red light or a green light is determined, and the traffic light state of the traffic light is not required to be identified by collecting images of the traffic light.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
fig. 1 is a schematic flow chart of a control method of an unmanned aerial vehicle in the present specification;
fig. 2 is a schematic diagram illustrating that the similarity between the first driving direction and the second driving direction falls within a first set similarity interval provided in the present specification;
fig. 3 is a schematic diagram illustrating that the similarity between the first driving direction and the second driving direction falls within a second set similarity interval provided in the present specification;
FIG. 4 is a schematic diagram of a control apparatus for an unmanned aerial vehicle provided herein;
fig. 5 is a schematic diagram of an unmanned device corresponding to fig. 1 provided by the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a control method of an unmanned aerial vehicle in this specification, and includes the following steps:
s101: determining target obstacles around the unmanned equipment and traffic lights corresponding to the positions of the unmanned equipment.
In the driving process of the unmanned driving equipment, the traffic light state of the traffic light in front needs to be accurately determined, namely, the traffic light is a red light or a green light, so that the unmanned driving equipment can be ensured to normally drive in a road like a common vehicle, and the safe driving of the unmanned driving equipment is ensured. In general, the unmanned device can acquire images of traffic lights to identify colors of the traffic lights in the images, so as to obtain states of the traffic lights, however, the states of the traffic lights cannot be judged through the images in some cases, and the states of the traffic lights can be judged through the method in such cases.
In this specification, the unmanned device may determine each target obstacle around and determine a traffic light corresponding to a position where the unmanned device is located, where the traffic light corresponding to the position where the unmanned device is located may refer to a traffic light that the unmanned device faces, that is, a traffic light in front of the unmanned device, and the traffic light is a traffic light that the unmanned device needs to travel according to color indication. The unmanned equipment can determine the obstacles existing around by acquiring the acquired point cloud data or acquiring images. Thereby determining each target obstacle.
The unmanned equipment mentioned above may refer to equipment capable of realizing automatic driving, such as unmanned vehicles, unmanned aerial vehicles, automatic distribution equipment, and the like. Based on this, the control method of the unmanned device provided by the specification can determine the traffic light state of the traffic light in front of the unmanned device in the driving process of the unmanned device, and control the unmanned device according to the traffic light state, and the unmanned device is particularly applicable to the field of distribution through the unmanned device, such as business scenes of distribution such as express delivery, logistics, takeaway and the like by using the unmanned device.
It should be noted that, since the driving state of each target obstacle is to be used to determine the actual traffic light state of the traffic light to which the unmanned aerial vehicle faces, it is necessary to determine a target obstacle suitable for determining the traffic light state of the traffic light from all the obstacles around the unmanned aerial vehicle.
Specifically, the unmanned device may obtain a preset electronic map, determine candidate obstacles around the unmanned device, then screen out interfering obstacles from the candidate obstacles according to preset screening conditions, acquired obstacle information of each candidate obstacle and the electronic map, and use other obstacles except the interfering obstacles in the candidate obstacles as target obstacles around the unmanned device, where the electronic map may be a high-precision map, and if a general navigation map includes detailed lane information, traffic light information, and the like, the electronic map may also be a general navigation map.
And after monitoring that the unmanned device is located in a set area containing a traffic light, the unmanned device may determine surrounding candidate obstacles located in the set area, where the size of the set area may be set according to actual needs, for example, a set area containing a traffic light may be a square area extending outward by 10m with the traffic light as a center.
That is, the unmanned device may start to determine surrounding candidate obstacles at a time when the unmanned device is about to reach the position of the traffic light, and the candidate obstacles determined in the above manner are obstacles closer to the traffic light.
It should be noted that the above mentioned interfering obstacles screened from the candidate obstacles may affect the accuracy of determining the traffic light state by the unmanned device, and specifically, the screening of the interfering obstacles may be performed in various ways.
For example, if an obstacle in the same direction as the unmanned aerial vehicle is stationary, the traffic light can be estimated as a red light from the state of the obstacle, but in the case of a green light, an obstacle such as a bicycle, a car, or a pedestrian that is parked on the roadside may erroneously recognize the traffic light state as a red light by the unmanned aerial vehicle, and in practice, such an interfering obstacle may be many. Therefore, for such a disturbing obstacle, it is necessary to exclude the disturbance to the unmanned aerial device determination of the traffic light state.
In practical applications, multiple kinds of interference obstacles may be determined, for example, the unmanned device may determine, for each candidate obstacle, that the candidate obstacle stays in a parking area on a roadside according to the electronic map, and may determine that the candidate obstacle is the interference obstacle.
For another example, for each candidate obstacle, if it is determined that the candidate obstacle is located at an intersection and turns according to a specified direction according to the acquired obstacle information of the candidate obstacle and the electronic map, it may be determined that the candidate obstacle is an interfering obstacle. The designated direction can be set according to actual requirements. Since in real life, a right turn on a road in some regions is not required to follow the indication of the traffic light, the specified direction may be set to a right turn, while a left turn may not be required to follow the indication of the traffic light in roads in other regions, and thus, the specified direction may be set to a left turn in these regions.
For another example, the unmanned device may determine, for each candidate obstacle, that the candidate obstacle is a non-motor vehicle and that the candidate obstacle is an interfering obstacle if the candidate obstacle is determined to be a non-motor vehicle according to the acquired obstacle information of the candidate obstacle. That is, this way leaves only the vehicles around the unmanned device that need to strictly comply with traffic regulations as target obstacles. The type of the candidate obstacle can be judged according to the obstacle information, such as an image corresponding to the candidate obstacle and point cloud data corresponding to the candidate obstacle, and if the type of the candidate obstacle is not a motor vehicle, the candidate obstacle is used as an interference obstacle. Of course, the interfering obstacle may also be determined by the type of each obstacle in the road recorded in the electronic map, for example, if a candidate obstacle is a street lamp, the type of the candidate obstacle may be determined by the electronic map, so that the candidate obstacle is used as the interfering obstacle.
For another example, for each candidate obstacle, if it is determined that the candidate obstacle and the unmanned equipment are located in different lanes according to the electronic map and the acquired obstacle information of the candidate obstacle, it is determined that the candidate obstacle is an interfering obstacle. The method is suitable for the situation that the traffic intersection comprises a plurality of traffic lights, and the unmanned equipment only needs to judge the traffic light state of the traffic light of the lane where the unmanned equipment is located.
In the following description, it is mentioned that, in the intersection, the traffic light state corresponding to the unmanned device can be determined by the driving state of the vehicle in the direction approximately perpendicular to the driving direction of the unmanned device, or by the driving state of the vehicle in the direction opposite to the driving direction of the unmanned device, and in the above, it is mentioned that the unmanned device can determine the traffic light state only by the target obstacle on the lane where the unmanned device is located. Therefore, in practical application, the unmanned device can determine whether to pass through the driving state of the target obstacle in the other lane according to the type of the traffic intersection where the traffic light is located, and determine the traffic light state of the traffic light corresponding to the position where the unmanned device is located.
For example, the unmanned device may determine, through the electronic map, a traffic intersection corresponding to the position of the unmanned device itself, and determine whether the traffic light states of each traffic light of the two-way roads in the traffic intersection are synchronous, if so, it is not necessary to use only the obstacle in the lane where the unmanned device itself is located as the target obstacle, and of course, if not, it is necessary to use only the obstacle in the lane where the unmanned device itself is located as the target obstacle, that is, if it is determined that one candidate obstacle and the unmanned device are located in different lanes, it is determined that the candidate obstacle is the interference obstacle.
S102: and aiming at each target obstacle, determining the current corresponding running state of the target obstacle according to the acquired obstacle information of the target obstacle.
S103: and determining the actual traffic light state corresponding to the traffic light according to the determined current corresponding driving state of each target obstacle.
After the unmanned device determines each target obstacle, the driving state of the target obstacle corresponding to the current state can be determined according to the acquired obstacle information of the target obstacle for each target obstacle.
The obstacle information of the target obstacle mentioned here may include an image of the acquired target obstacle, point cloud data acquired for the obstacle, a determined travel track of the obstacle, and the like. The driving state of the target obstacle at the present time referred to herein can indicate whether the target obstacle is currently moving or in a stopped state. That is, the traveling state of the target obstacle at the present time may be a moving state or a stopped state, but of course, the traveling state of the target obstacle may be a traveling locus of the target obstacle, and the traveling locus may indicate whether the target obstacle is traveling or stopped.
The position of the target obstacle at each moment can be determined through the acquired images or the point cloud data, and the position of the target obstacle at each moment is also determined for the driving track of the target obstacle, so that the driving state of the target obstacle can be determined through the data, and then the unmanned equipment can determine the actual traffic light state corresponding to the traffic light according to the determined driving state of each target obstacle at the current time.
In this specification, the unmanned aerial vehicle may determine, for each target obstacle, a candidate traffic light state of a traffic light corresponding to a position where the unmanned aerial vehicle is located according to a current driving state of the target obstacle, and then determine, according to the candidate traffic light state corresponding to the traffic light determined for each target obstacle, an actual traffic light state corresponding to the traffic light. That is, with each target obstacle, the traffic light state that the unmanned aerial vehicle can indicate the traffic light that the unmanned aerial vehicle faces according to the driving state of the target obstacle can be estimated by the unmanned aerial vehicle as the traffic light candidate state determined according to the current driving state of the target obstacle. Then, each target obstacle corresponds to the traffic light state estimated by the unmanned aerial vehicle as a candidate traffic light state. The unmanned equipment can take the candidate traffic light states with a large number as the actual traffic light states corresponding to the traffic lights, and can also take the candidate traffic light states with a larger proportion as the actual traffic light states corresponding to the traffic lights.
For each target obstacle, the unmanned device needs to determine a candidate traffic light state according to a current driving state of the target obstacle, where the candidate traffic light state may be whether a traffic light state of a traffic light in front of the unmanned device, which is indicated by the driving state of the target obstacle, is a red light or a green light. Specifically, the drone may determine a current direction of travel of the target obstacle as a first direction of travel and determine a current direction of travel of the drone as a second direction of travel.
If the unmanned device determines that the similarity between the first driving direction and the second driving direction falls into a first set similarity interval, when the target obstacle is in a moving state at present, the candidate traffic light state of the traffic light corresponding to the position where the unmanned device is located can be determined to be a green light, and when the target obstacle is in a static state at present, the candidate traffic light state corresponding to the traffic light can be determined to be a red light.
That is, under such conditions, the traffic light state of the traffic light in front of the target obstacle coincides with the traffic light state of the traffic light in front of the unmanned aerial device, so that the traffic light candidate state is green light when the target obstacle is traveling, and the traffic light candidate state is red light when the target obstacle is in a stopped state. The first set similarity degree section mentioned here may be set in advance.
For example, assuming that the similarity between the first traveling direction and the second traveling direction is a cosine similarity, a first set similarity interval in which the first traveling direction is about the same direction as the second traveling direction may be set to [0.9,1], and a range in which the first traveling direction is about the opposite direction to the second traveling direction may be set to [ -0.9, -1 ]. That is, in both cases, the target obstacle may be in the same direction or opposite direction of the road as the unmanned aerial vehicle, and thus, the traffic light candidate state should be a green light when the target obstacle is traveling, and the traffic light candidate state should be a red light when the target obstacle is stopped, as shown in fig. 2.
Fig. 2 is a schematic diagram of a similarity between a first driving direction and a second driving direction falling within a first set similarity interval provided in the present specification.
As can be seen from fig. 2, since the angle between the driving direction 1 and the driving direction 2 is 0 degree, and the cosine similarity between the driving direction 1 and the driving direction 2 is 1, which fall into the first set similarity interval in the above example, the unmanned device is in the same direction as the target obstacle 1, and therefore, if the target obstacle 1 is in a moving state, the candidate traffic light state determined according to the target obstacle 1 is a green light, and otherwise, the candidate traffic light state is a red light. Similarly, the target obstacle 2 is also similar, the unmanned device is opposite to the target obstacle 2, the included angle between the driving direction 1 and the driving direction 3 is 180 degrees, the cosine similarity is-1, and the included angle also falls into the first set similarity interval in the above example, so that if the target obstacle 2 is in a moving state, the candidate traffic light state determined according to the target obstacle 2 is a green light, and otherwise, the candidate traffic light state is a red light.
If the unmanned device determines that the similarity between the first driving direction and the second driving direction falls into a second set similarity interval, when the target obstacle is in a moving state at present, the candidate traffic light state of the traffic light corresponding to the position where the unmanned device is located can be determined to be a red light, and when the target obstacle is in a static state at present, the candidate traffic light state corresponding to the traffic light can be determined to be a green light.
That is, under this condition, the traffic light state of the traffic light that the unmanned aerial vehicle faces is exactly opposite to the traffic light state of the traffic light that the target obstacle faces, and when the traffic light state of the traffic light that the unmanned aerial vehicle faces is a green light, the traffic light state corresponding to the traffic light that the unmanned aerial vehicle faces is a red light, and when the traffic light state of the traffic light that the target obstacle faces is a red light, the traffic light state corresponding to the traffic light that the unmanned aerial vehicle faces is a green light. Therefore, the traffic light candidate state should be red when the target obstacle is traveling, and the traffic light candidate state should be green when the target obstacle is at rest.
The second set similarity interval mentioned above may also be set according to actual requirements, for example, the second set similarity interval may be set to [ -0.1,0.1], that is, the second set similarity interval indicates that the included angle between the first driving direction and the second driving direction is about 90 degrees, and the target obstacle may be in a cross road with respect to the road on which the unmanned equipment is located, as shown in fig. 3.
Fig. 3 is a schematic diagram of a similarity between the first driving direction and the second driving direction falling within a second set similarity interval provided in the present specification.
As can be seen from fig. 3, in the intersection, the unmanned aerial vehicle and the target obstacle 3 are in the lanes perpendicular to each other, the angle between the traveling direction 4 and the traveling direction 5 is 90 degrees, and the cosine similarity between the traveling direction 4 and the traveling direction 5 is 0, which falls into the second set similarity interval in the above example. Therefore, if the target obstacle 3 is in a moving state, the traffic light candidate determined from the target obstacle 3 is in a red light state, and otherwise, it is in a green light state.
In this specification, when the target obstacle is in a moving state, after it is determined that the target obstacle is located at a traffic intersection corresponding to the unmanned device, a candidate traffic light state may be determined according to a current driving state of the target obstacle. That is, when the target obstacle is currently in a moving state, it needs to be determined that the target obstacle is located in a traffic intersection where a traffic light corresponding to a position where the unmanned device is located before determining a traffic light candidate state corresponding to the traffic light.
The reason why it is necessary to determine that the target obstacle is located in the traffic intersection corresponding to the target traffic light is that some vehicles, which are located outside the traffic intersection, slowly travel to the stop line and stop before reaching the stop line at the red light. If these vehicles are not excluded, it may be possible to erroneously determine that the actual traffic light status is green because these vehicles are moving when the actual traffic light status of the traffic light in front of the drone is red. Therefore, for a target obstacle in a moving state, the candidate traffic light state may be determined only by the target obstacle within the traffic intersection. The target obstacle located in the intersection referred to herein means a target obstacle located inside the stop line of the traffic intersection, that is, the area framed by the broken line in fig. 2 or 3 is the intersection.
In this specification, when a target obstacle is currently in a stationary state, before determining a traffic light candidate state corresponding to the traffic light, it may be determined that there is no other obstacle blocking the travel of the target obstacle within a set distance in front of the target obstacle, that is, the target obstacle in the stationary state, and if the obstacle is not blocked by another obstacle, the traffic light candidate state may be determined from the current travel state of the target obstacle.
In other words, this condition is to determine whether the target obstacle was stopped because of the presence of a red light in front of the target obstacle, and not because of traffic congestion or other conditions. In addition, in practical application, when the traffic light is just switched to the green light, some motor vehicles may start slowly, so that subsequent motor vehicles can not run in place, and if the unmanned equipment determines the candidate traffic light state through the blocked motor vehicles, the determined actual traffic light state is probably mistaken as the red light, but actually is the green light. Therefore, by determining that there is no condition for obstructing another obstacle traveling by the target obstacle within a set distance in front of the target obstacle, it is possible to effectively prevent this. It will be appreciated that the other obstacle may be a motor vehicle, but it is also possible that the other obstacle may be another type of obstacle, such as a person, a bicycle, etc.
S104: and controlling the unmanned equipment according to the actual traffic light state.
After the actual traffic light state of the traffic light corresponding to the current position of the unmanned device is determined according to the current corresponding running state of each target obstacle, the unmanned device can control the unmanned device according to the actual traffic light state. That is, if the actual traffic light state is a green light, the unmanned aerial vehicle can travel as usual, and if the actual traffic light state is a red light, the unmanned aerial vehicle needs to stop before reaching the stop line or stop after the preceding vehicle.
According to the method, when the traffic light state of the traffic light in front of the unmanned equipment cannot be judged through image recognition, the traffic light state of the traffic light in front can be determined through the driving states of all the surrounding target obstacles through the method, namely, the unmanned equipment can be used for presuming the traffic light state of the traffic light in front through surrounding vehicles which need to drive according to traffic rules, and the accuracy of the determined actual traffic light state is further ensured by eliminating some obstacles which can cause the unmanned equipment to judge the traffic light state wrongly.
Based on the same idea, the present specification further provides a control device of the unmanned aerial vehicle, as shown in fig. 4.
Fig. 4 is a schematic diagram of a control device of an unmanned aerial vehicle provided in the present specification, including:
the obstacle determining module 401 is configured to obtain a preset electronic map, determine candidate obstacles around the unmanned aerial vehicle, screen interference obstacles from the candidate obstacles according to preset screening conditions, acquired obstacle information of each candidate obstacle and the electronic map, use other obstacles except the interference obstacles in the candidate obstacles as target obstacles around the unmanned aerial vehicle, and determine a traffic light corresponding to a position where the unmanned aerial vehicle is located;
a driving state determining module 402, configured to determine, for each target obstacle, a current corresponding driving state of the target obstacle according to the acquired obstacle information of the target obstacle;
a traffic light state determining module 403, configured to determine, according to a current corresponding driving state of each determined target obstacle, an actual traffic light state corresponding to the traffic light;
and a control module 404, configured to control the unmanned device according to the actual traffic light state.
Optionally, the obstacle determining module 401 is specifically configured to determine, if it is monitored that the unmanned aerial vehicle is located in a set area including a traffic light, candidate obstacles around the unmanned aerial vehicle and located in the set area.
Optionally, the obstacle determining module 401 is specifically configured to, for each candidate obstacle, determine that the candidate obstacle is located at an intersection and turns according to a specified direction if the candidate obstacle is located at the intersection according to the acquired obstacle information of the candidate obstacle and the electronic map, and determine that the candidate obstacle is an interfering obstacle; or aiming at each candidate obstacle, if the candidate obstacle stays in a roadside parking area according to the electronic map, determining the candidate obstacle as an interference obstacle; or for each candidate obstacle, if the candidate obstacle is determined to be a non-motor vehicle according to the acquired obstacle information of the candidate obstacle, determining the candidate obstacle to be an interference obstacle; or for each candidate obstacle, if the candidate obstacle and the unmanned equipment are located in different lanes according to the electronic map and the acquired obstacle information of the candidate obstacle, determining that the candidate obstacle is an interference obstacle.
Optionally, the traffic light state determining module 403 is specifically configured to determine, for each target obstacle, a candidate traffic light state corresponding to the traffic light according to a current driving state of the target obstacle; and determining the actual traffic light state corresponding to the traffic light according to the candidate traffic light state corresponding to the traffic light determined aiming at each target obstacle.
Optionally, the traffic light status determining module 403 is specifically configured to determine a current driving direction of the target obstacle as a first driving direction, and determine a current driving direction of the unmanned device as a second driving direction; if the similarity between the first driving direction and the second driving direction is determined to fall into a first set similarity interval, when the target obstacle is in a moving state, determining that the candidate traffic light state corresponding to the traffic light is a green light, and when the target obstacle is in a static state, determining that the candidate traffic light state corresponding to the traffic light is a red light; if the similarity between the first driving direction and the second driving direction is determined to fall into a second set similarity interval, when the target obstacle is in a moving state, the candidate traffic light state corresponding to the traffic light is determined to be a red light, and when the target obstacle is in a static state, the candidate traffic light state corresponding to the traffic light is determined to be a green light.
Optionally, when the target obstacle is currently in a moving state, and before determining the candidate traffic light state corresponding to the traffic light, the traffic light state determination module 403 is further configured to determine that the target obstacle is located at the traffic intersection corresponding to the traffic light.
Optionally, before the traffic light state determination module 403 determines the candidate traffic light state corresponding to the traffic light when the target obstacle is currently in a stationary state, the traffic light state determination module 403 is further configured to determine that no other obstacle blocking the target obstacle from traveling exists within a set distance in front of the target obstacle.
The present specification also provides a computer-readable storage medium storing a computer program operable to execute the control method of an unmanned aerial vehicle provided in fig. 1 described above.
The present specification also provides a schematic block diagram of an unmanned aerial device corresponding to that of figure 1, shown in figure 5. As shown in fig. 5, the drone includes, at a hardware level, a processor, an internal bus, a network interface, a memory, and a non-volatile memory, although it may also include hardware needed for other services. The processor reads a corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the method for controlling the unmanned aerial vehicle described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A control method of an unmanned aerial vehicle, characterized by comprising:
acquiring a preset electronic map, determining candidate obstacles around the unmanned equipment, screening interference obstacles from the candidate obstacles according to preset screening conditions, acquired obstacle information of each candidate obstacle and the electronic map, taking other obstacles except the interference obstacles in the candidate obstacles as target obstacles around the unmanned equipment, and determining traffic lights corresponding to the position where the unmanned equipment is located;
aiming at each target obstacle, determining the current corresponding running state of the target obstacle according to the acquired obstacle information of the target obstacle;
determining the actual traffic light state corresponding to the traffic light according to the determined current corresponding driving state of each target obstacle;
and controlling the unmanned equipment according to the actual traffic light state.
2. The method of claim 1, wherein determining candidate obstacles around the drone specifically comprises:
and if the unmanned equipment is monitored to be located in a set area containing traffic lights, determining each candidate obstacle around the unmanned equipment and located in the set area.
3. The method according to claim 1, wherein the step of screening the interference obstacle from the candidate obstacles according to a preset screening condition, the acquired obstacle information of each candidate obstacle and the electronic map specifically comprises:
for each candidate obstacle, if the candidate obstacle is determined to be positioned at the intersection according to the acquired obstacle information of the candidate obstacle and the electronic map, and steering is performed according to the specified direction, the candidate obstacle is determined to be an interference obstacle; or
For each candidate obstacle, if the candidate obstacle stays in a roadside parking area according to the electronic map, determining the candidate obstacle as an interference obstacle; or
For each candidate obstacle, if the candidate obstacle is determined to be a non-motor vehicle according to the acquired obstacle information of the candidate obstacle, determining the candidate obstacle to be an interference obstacle; or
And for each candidate obstacle, if the candidate obstacle and the unmanned equipment are positioned in different lanes according to the electronic map and the acquired obstacle information of the candidate obstacle, determining that the candidate obstacle is an interference obstacle.
4. The method of claim 1, wherein determining the actual traffic light state corresponding to the traffic light according to the determined current driving state of each target obstacle comprises:
aiming at each target obstacle, determining a candidate traffic light state corresponding to the traffic light according to the current corresponding running state of the target obstacle;
and determining the actual traffic light state corresponding to the traffic light according to the candidate traffic light state corresponding to the traffic light determined aiming at each target obstacle.
5. The method of claim 4, wherein determining the candidate traffic light status corresponding to the traffic light according to the current driving status corresponding to the target obstacle comprises:
determining a current driving direction of the target obstacle as a first driving direction, and determining a current driving direction of the unmanned device as a second driving direction;
if the similarity between the first driving direction and the second driving direction is determined to fall into a first set similarity interval, when the target obstacle is in a moving state, determining that the candidate traffic light state corresponding to the traffic light is a green light, and when the target obstacle is in a static state, determining that the candidate traffic light state corresponding to the traffic light is a red light;
if the similarity between the first driving direction and the second driving direction is determined to fall into a second set similarity interval, when the target obstacle is in a moving state, the candidate traffic light state corresponding to the traffic light is determined to be a red light, and when the target obstacle is in a static state, the candidate traffic light state corresponding to the traffic light is determined to be a green light.
6. The method of claim 5, wherein when the target obstacle is currently in a moving state, prior to determining the candidate traffic light state corresponding to the traffic light, the method further comprises:
and determining that the target barrier is positioned in the traffic intersection corresponding to the traffic light.
7. The method of claim 5, wherein when the target obstacle is currently in a stationary state, prior to determining the candidate traffic light state corresponding to the traffic light, the method further comprises:
and determining that no other obstacles for blocking the target obstacle to run exist within the set distance in front of the target obstacle.
8. A control apparatus of an unmanned aerial vehicle, characterized by comprising:
the obstacle determining module is used for acquiring a preset electronic map, determining candidate obstacles around the unmanned equipment, screening interference obstacles from the candidate obstacles according to preset screening conditions, acquired obstacle information of each candidate obstacle and the electronic map, taking other obstacles except the interference obstacles in the candidate obstacles as target obstacles around the unmanned equipment, and determining traffic lights corresponding to the position where the unmanned equipment is located;
the driving state determining module is used for determining the current corresponding driving state of each target obstacle according to the acquired obstacle information of the target obstacle;
the traffic light state determining module is used for determining the actual traffic light state corresponding to the traffic light according to the determined driving state of each target barrier at the current time;
and the control module is used for controlling the unmanned equipment according to the actual traffic light state.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 7.
10. An unmanned aerial vehicle comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of any of claims 1 to 7.
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